call for proposals - /media/corporate/pdf/hirp/2016--future... · unless otherwise agreed by huawei...

HIRP OPEN 2016 Future Networks

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Call for Proposals

Future Networks

HIRP OPEN 2016


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Copyright © Huawei Technologies Co., Ltd. 2015-2016. All rights reserved.

No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd.

Trademarks and Permissions

and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd.

All other trademarks and trade names mentioned in this document are the property of their respective holders.

Confidentiality

All information in this document (including, but not limited to interface protocols, parameters, flowchart and formula) is the confidential information of Huawei Technologies Co., Ltd and its affiliates. Any and all recipient shall keep this document in confidence with the same degree of care as used for its own confidential information and shall not publish or disclose wholly or in part to any other party without Huawei Technologies Co., Ltd’s prior written consent.

Notice

Unless otherwise agreed by Huawei Technologies Co., Ltd, all the information in this document is subject to change without notice. Every effort has been made in the preparation of this document to ensure accuracy of the contents, but all statements, information, and recommendations in this document do not constitute the warranty of any kind, express or implied.

Distribution

Without the written consent of Huawei Technologies Co., Ltd, this document cannot be distributed except for the purpose of Huawei Innovation R&D Projects and within those who have participated in Huawei Innovation R&D Projects.

Application Deadline: 09:00 A.M., 18th July, 2016 (Beijing Standard Time, GMT+8).

If you have any questions or suggestions about HIRP OPEN 2016, please send Email

([email protected]). We will reply as soon as possible.

mailto:[email protected]).


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Catalog

HIRPO20160201: Mobility Research for High-Frequency Network .......................................... 5

HIRPO20160202: Wireless Power Supply for Low Power Consumption Equipment by Base

Station ........................................................................................................................................ 8

HIRPO20160203: Research on Key Technology of Transmission of High Definition Video for

UAV ......................................................................................................................................... 12

HIRPO20160204: Research on Key Technology of Virtual Reality using Wireless

Communication ........................................................................................................................ 15

HIRPO20160205: Research on Wireless Communication Network for Robotic Applications 17

HIRPO20160206: Research on Ambient Backscatter Wireless Communication Technology 19

HIRPO20160207: Research on Haze Suppression Using Electromagnetic Wave

Agglomeration .......................................................................................................................... 22

HIRPO20160208: Auto-Scaling and Resource Coordination of Network Slices ..................... 25

HIRPO20160209: Carrier Grade Cloud Resource Management based on Deep Learning

Technology .............................................................................................................................. 28

HIRPO20160210: Game Theory based Network Slicing Management .................................. 30

HIRPO20160211: Resource Allocation and Mapping for Network Slices ............................... 33


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HIRPO20160212: Trajectory Modeling and Generation for Mobile Users .............................. 36


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HIRPO20160201: Mobility Research for

High-Frequency Network

1 Theme: Future Networks

2 Subject: architecture and resource management

List of Abbreviations

NR: New RAT

RLF: Radio Link Failure

RRM: Radio Resource Management

BRS: Beam Reference Signal

3 Background

The new RAT (NR) will consider frequency ranges up to 100 GHz. The radio

characteristics of NR may include large path loss, enlarged noise power (due

to large bandwidth), small cell coverage and large signal variation. To

overcome high frequency channel condition, the beamforming technology may

be used in some scenarios. However in general one beam has relatively

narrow coverage. Also the UE may be receiving more than one beam in a cell.

This brings huge challenges to mobility management, e.g. beam acquisition,

beam tracking, beam-based RRM measurement, RLF detection.

Moreover, this small coverage and fragile channel characteristic of NR lead to

frequent handovers and handover failures which UE experiences. Ping-pong

also happens more frequently in NR. As a result, mobility performance will be


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degraded unless mobility mechanism is improved in NR compared to the

current LTE.

So, it is a valuable research direction to investigate the measurement and

mobility mechanism in high frequency to ensure good mobility performance in

NR.

4 Scope

Beam-based RRM measurement and RLF detection mechanisms

Investigate the beam-based RRM measurement and RLF detection

mechanisms, including beam acquisition, beam tracking, beam-based RRM

measurement (e.g. BRS design, how to measure, how to be averaged and

how to trigger measurement report), RLF detection, avoid and/or quickly react

to sudden SINR drops due to beamforming, and so on;

Mobility mechanisms in high frequency

Investigate the mobility mechanisms in high frequency, including how to

decrease the handover failure and Ping-Pong rate, ensuring 0ms handover

interruption time, and so on;

5 Expected Outcome and Deliverables

Technical reports of beam-based RRM measurement and RLF detection

mechanisms;

Technical reports of mobility mechanisms in high frequency;

Related simulation platform with source codes and description;

1~2 Invention/patents.


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6 Acceptance Criteria

Design competitive measurement and mobility mechanisms in high frequency

and ensure good mobility performance at least not worse than LTE.

7 Phased Project Plan

Phase1 (~3 months): Survey the state of the art of measurement and mobility

solutions in high frequency especially with beamforming technology, analyze

and provide the related technical report;

Phase2 (~6 months): Research on schemes of measurement and mobility,

including beam-based RRM measurement, RLF detection and handover, and

provide the related technical report;

Phase3 (~3 months): Research and provide related algorithms, simulation

results and patents.

Click here to back to the Top Page


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HIRPO20160202: Wireless Power Supply for Low

Power Consumption Equipment by Base Station


2 Subject: IRF


RFID: Radio Frequency IDentification

3 Background

Limited device battery life has always been a key consideration in the design of

modern mobile wireless technologies. Frequent battery

replacement/recharging is often costly due to the large number of wireless

devices in use, and even infeasible in many critical applications (e.g., sensors

embedded in structures and implanted medical devices).

Categories and Applications

RF-enabled wireless energy transfer (WET) technology provides an attractive

solution by powering wireless devices with continuous and stable energy over

the air. By leveraging the far-field radiative properties of electromagnetic (EM)

waves, wireless receivers could harvest energy remotely from RF signals

radiated by an energy transmitter. RF-enabled WET enjoys many practical

advantages, such as wide operating range, low production cost, small receiver

form factor, and efficient energy multicasting thanks to the broadcast nature of

EM waves.

One important application of RF-enabled WET is wireless powered

communication (WPC), where wireless devices use harvested RF energy to


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transmit/decode information to/from other devices. Without being interrupted

by energy depletion due to communication usage, WPC is expected to

improve user experience and convenience, with higher and more sustainable

throughput performance than conventional battery-powered communication.

WPC can also be applied in sensors with much lower maintenance cost and

enhanced flexibility in practical deployment.

Due to the high attenuation of microwave energy over distance, RF-enabled

WET is commonly used for supporting low power devices, such as RFID tags

and sensors. However, recent advances in antenna technologies and RF

energy harvesting circuits have enabled much higher microwave power to be

efficiently transferred and harvested by wireless devices. Therefore, WPC will

be an important building block of many popular commercial and industrial

systems in the future, including the upcoming Internet of Things/Everything

(IoT/IoE) systems consisting of billions of sensing/RFID devices as well as

large-scale wireless sensor networks (WSNs).

Model of (Power + Communication) vs. Model of Energy Harvesting

We also envision RF-enabled WET as a key component of the “last-mile”

power delivery system, with the smart electrical power gird forming the

backbone or core power network. Before proceeding to the discussion of

RFenabled WET/WPC, it is worth pointing out its relation to another green

communication technique, energy harvesting (EH), where wireless devices

harness energy from energy sources in the environment not dedicated to

powering wireless devices, such as solar power, wind power, and ambient EM

radiation.

Unlike RF-based EH from ambient transmitters, the energy source of WET is

stable and, more importantly, fully controllable in its transmit power, waveforms,

and occupied time/frequency dimensions to power the energy receivers. With


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a controllable energy source, a WPC network (WPCN) could be efficiently built

to power multiple communication devices with different physical conditions and

service requirements. Besides, with RF enabled WET, information could also

be jointly transmitted with energy using the same waveform. Such a design

paradigm is referred to as simultaneous wireless information and power

transfer (SWIPT), which has proved to be more efficient in spectrum usage

than transmitting information and energy in orthogonal time or frequency

channels.

4 Scope

Investigate wireless charging technology;

Research on marco base station based wireless charging technology,

which aims to provide power for low cost devices.


1 survey reports on key technology;

Research on marco base station based wireless charging technology,

which aims to provide power for low cost devices at the power level of

0.001w;

1-2 patents and 1 publication submission;

1 prototype of technical identification.


Survey Report: Comprehensive study of the subject;


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Research Report/Design Report: Technical solution can be implemented.

Clear technological advancement can be proved. Clear advancement can be

proved;

Patent Proposal: Patent proposals are evaluated and accepted by the internal

Huawei patent evaluation;

Publication: Paper written and submitted to a prestigious conference.


Phase1 (~3 months): Survey on technology of the wireless power supply;

Phase2 (~6months): Explore the technology of the wireless power supply for

low power consumption equipment by base station;

Phase3 (~3 months): Use case study and solution study.



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HIRPO20160203: Research on Key Technology of

Transmission of High Definition Video for UAV


2 Subject: radio transmission technology

3 Background

Now Unmanned Aerial Vehicle (UAV) is a very hot topic, and there are already

many UAV both in the consumer market and civilian market. One typical use

case of UAV is remote monitor, which will send back the real-time HD video to

the monitor center. How to use the existing wireless communication

technology or new to transfer the VR contents is an interesting problem.

4 Scope

Identify the typical requirements for the HD video transmission for UAV;

Survey on the transmission technology of high definition video for UAV

when using wireless communication;

Impact on wireless communication when using HD video;

Research on a new network architecture to fit for high definition video

transmission for UAV;

Research on coverage enhancement/experience improvement solution

design: based on the typical UAV HD video transmission use cases, and

identified requirements, design the solutions to effectively enhance the HD

video coverage and transmission reliability, within the latency and energy

limitation.


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1 survey reports on key technology of transmission of high definition video

for UAV;

1-2 research reports on key technology, including license/un-license

spectrum，candidate schemes of optimal technology used in wireless

communication;

1 design/analysis reports and verification about key technology and

system architecture;

1-2 patents and 1 publication submission;

1 prototype of technical identification.




Clear technological advancement can be proved. Clear advancement can

be proved;

Patent Proposal: Patent proposals are evaluated and accepted by the

internal Huawei patent evaluation;



Phase 1 (~2 months): Survey on key technology of transmission of high

definition video for UAV, including industry and academia area;


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Phase 2 (~7 months): Research on key technology of transmission of high

definition video for UAV, including architecture design, model selection,

algorithm design and so on;

Phase 3 (~3 months): Verification of the proposed architecture and technology.



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HIRPO20160204: Research on Key Technology of

Virtual Reality using Wireless Communication


2 Subject: others

3 Background

Now virtual reality or augmented reality is a very hot topic, and there are many

VR devices in the commercial market. Because of the inherent character of VR

contents, the amount of transport is very large, so now the connection between

VR server and display headset is all fixed, which will restrict the mobility when

playing VR game or using other VR services. How to use the wireless

communication to transfer the VR contents is an interesting problem.

4 Scope

Survey on the AR/VR technology when using wireless communication;

Impact on wireless communication when using VR services;

Analysis of the requirement for wireless network for different AR

experience;

Research on a new network architecture to fit for VR transport.


1 survey reports on key technology of virtual reality using wireless

communication;


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1-2 research reports on key technology of virtual reality, including

candidate schemes of optimal technology used in wireless communication;

1 design/analysis reports and verification about key technology of virtual

reality using wireless communication, such as system architecture;

1-2 patents and 1 publication submission.





be proved;





Phase1 (~2 months): Survey on key technology of virtual reality using wireless

communication, including industry and academia area;

Phase2 (~7 months): Research on key technology of virtual reality using

wireless communication, including architecture design, model selection,





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HIRPO20160205: Research on Wireless

Communication Network for Robotic Applications



3 Background

Robotic application has been an emerging area in both enterprise and

consumer business. There is also ongoing standardization work in 3GPP for

related scenarios, e.g. NB-IoT and/or mMTC. However there may be more

specific verticals that require different user experience and functionalities, such

as cooperative robotics in a Multi Agent System. How to use wireless

technologies to enable more exciting applications would be a promising area.

4 Scope

Survey on the robotic technology when using wireless communication;

Impact on wireless communication when implementing robotic services;

Analysis of the requirement for wireless network for different robotic

applications;

Research on a new network architecture to fit for robotics traffic.


1 survey reports on key technology of robotics using wireless

communication;

1-2 research reports on key technology of robotics, including candidate

schemes of optimal technology used in wireless communication;


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1 design/analysis reports and verification about key technology of robotics

using wireless communication, such as system architecture;






be proved;





Phase 1 (~2 months): Survey on key technology of robotics using wireless

communication, including industry and academia area;

Phase 2 (~7 months): Research on key technology of robotics using wireless

communication, including architecture design, model selection, algorithm

design and so on.




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HIRPO20160206: Research on Ambient Backscatter

Wireless Communication Technology


2 Subject: radio transmission technology

3 Background

In traditional backscatter communication (e.g., RFID), a device communicates

by modulating its reflections of an incident RF signal (and not by generating

radio waves). Hence, it is orders of magnitude more energy-efficient than

conventional radio communication.

Ambient backscatter differs from RFID-style backscatter in three key respects.

Firstly, it takes advantage of existing RF signals so it does not require the

deployment of a special-purpose power infrastructure—like an RFID

reader—to transmit a high-power (1W) signal to nearby devices. This

avoids installation and maintenance costs that may make such a system

impractical, especially if the environment is outdoors or spans a large area;

Second, and related, it has a very small environmental footprint because

no additional energy is consumed beyond that which is already in the air;

Finally, ambient backscatter provides device-to-device communication.

This is unlike traditional RFID systems in which tags must talk exclusively

to an RFID reader and are unable to even sense the transmissions of other

nearby tags.

Designing an ambient backscatter system is challenging for at least three

reasons.


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Since backscattered signals are weak, traditional backscatter uses a

constant signal to facilitate the detection of small level changes. Ambient

backscatter uses uncontrollable RF signals that already have information

encoded in them. Hence it requires a different mechanism to extract the

backscattered information;

Traditional backscatter receivers rely on power-hungry components such

as oscillators and ADCs and decode the signal with relatively complex

digital signal processing techniques. These techniques are not practical for

use in a battery-free receiver;

Ambient backscatter lacks a centralized controller such as an RFID reader

to coordinate all communications. Thus, it must operate a distributed

multiple access protocol and develop functionalities like carrier sense that

are not available in traditional backscattering devices.

4 Scope

Survey on the Ambient Backscatter wireless communication technology;

Explore the maximum transmission distance and transmission rate via

Ambient Backscatter technology;

Use case study and solution study using Ambient Backscatter technology.


One survey reports on key technology of Ambient Backscatter wireless

communication;

One or two research reports on key technology of Ambient Backscatter

wireless communication technology;


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One design/analysis reports and verification about key technology of

Ambient Backscatter wireless communication technology, such as system

architecture;

One or two patents and 1 publication submission.





be proved;





Phase1 (~3 months): Survey on the Ambient Backscatter wireless

communication technology;

Phase2 (~6 months): Explore the maximum transmission distance and

transmission rate via Ambient Backscatter technology;

Phase3 (~3 months): Use case study and solution study using Ambient

Backscatter technology.



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HIRPO20160207: Research on Haze Suppression

Using Electromagnetic Wave Agglomeration


2 Subject: IRF


PM 2.5: Particulate Matter with diameter smaller than 2.5μm

EMW: Electromagnetic Wave

3 Background

Haze weather is growing to be a global issue, which not only decreases the

traffic visibility, but also causes disease among a large number of individuals,

especially in urban area. Traditional methods of suppressing haze can merely

be used in a confined space, usually connected to a stove, which is widely

applied in coal fields. With respect to urban area, however, the haze created by

plenty of sources including automobile exhaust as well as the residues of dust

and powder from surrounding factories, is everywhere in the open space. Even

though the indoor air cleaner can exert some function, the expensive price for

an average family is a financial burden; and furthermore, the majority of people

should go outside in daily life. Therefore, a haze suppression approach for

open space is significant and emergency for protecting the environment

particularly in a heavy haze day.

Electromagnetic wave (EMW), widely used in wireless communication,

broadcasting, radar, etc., is nearly the only technology that propagates freely in

the open space and tends to be safe by controlling the transmit power. The


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implement of approaching EMW to haze suppression could make great

benefits for both public health and commercial profit. Research on this

possibility is, in a word, valuable.

4 Scope

1) Valid parameters of the EMW for haze suppression: the parameter scope of

the EMW which is valid for suppressing the concentration of PM2.5, especially

the power and frequency instigating more than 20% of the PM2.5 particles

agglomerated to PM10, should be obtained with experiment and/or theoretic

proof;

2) Charging scheme on the base station: for the outdoor haze weather, the

particles should be charged sufficient for electrical agglomeration;

3) Device design: minimizing the size and power of the device for the particle

agglomeration, for being settled on base station.


1 survey reports on key technology of the EMW for haze suppression;

1-2 research reports on key technology of the EMW for haze suppression;

1 design/analysis reports and verification about key technology of the EMW for

haze suppression;





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Clear technological advancement can be proved. Clear advancement can be

proved;





Phase1 (~3 months): Researching and preparing for the experiments;

Phase2 (~6 months): Testing and searching for the valid parameter scopes of

the EMW;

Phase3 (~3 months): Compiling the reports and intellectual property material.



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HIRPO20160208: Auto-Scaling and Resource

Coordination of Network Slices




NFV: Network Function Virtualization

SDN: Software Defined Network

3 Background

A digital transformation, brought by the power of connectivity, is taking place in

almost every industry. New Communication types like Vehicle-to-Vehicle and

Machine-to-Machine will emerge with different network requirements. Network

slicing will enable operators to provide networks on an xyz-as-a-service basis.

By leveraging SDN and NFV, dedicated network slices will be created on the

same physical infrastructure. Network operators should make proper decisions

based on algorithms about the lifecycle management to meet the service

requirements and enhance resource utilization.

4 Scope

Survey on characteristics of different network slices and topics of network

slice resource management, resource allocation and coordination

algorithms;

Research on service requirement of different network slices. Feature

extraction and trend prediction of service workload and resource workload;


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Definition of network attributes in network slice and setup mathematical

models;

Design auto-scaling algorithms and resource coordination algorithms

between network slices to satisfy carrier-grade reliability requirement and

enhance physical resource utilization.


1 survey reports on auto-scaling and network resource coordination;

1-2 research reports on auto-scaling architecture design, including

candidate schemes of optimal resource coordination for network slices;

2 algorithms analysis reports about auto-scaling and resource

coordination;




Research Report/Algorithm Report: Technical solution can be implemented.

Clear technology and algorithm advancement can be proved;





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Phase 1 (~2 months): Characteristics of different network slices and topics of

network slice resource management, resource allocation or resource

coordination algorithms;

Phase 2 (~3 months): Research on service requirement of different network

slices. Feature extraction and trend prediction of service workload and

resource workload;

Phase 3 (~7 months): Definition of network attributions in network slice and

setup mathematical models. Design auto-scaling algorithms and resource

coordination algorithms between network slices in network slice to satisfy

carrier-grade reliability requirement and enhance physical resource utilization.



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HIRPO20160209: Carrier Grade Cloud Resource

Management based on Deep Learning Technology




DL: Deep Learning

3 Background

Deep learning (DL) is widely used in natural language processing, image

recognition, text recognition and other fields currently. While operating the

Telco networks, there are many system performance data and log information

that can be collected. The expert system will run resource management and

failure analysis from these data. But this method is inefficient and difficult to

adapt the revolution of complex telecommunication networks. It is a major

research topic of how to use machine learning algorithms or deep learning

algorithms to identify potential risks in telecommunications networks.

4 Scope

Survey on deep learning technology, especially in telecommunication

management;

Describe and design the architecture, model and key technology of

telecommunication management with deep learning;

Research on the Telco cloud resource management, failure prediction (not

limited) assisted by deep learning.


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1 survey reports on carrier grade cloud resource management based on

deep learning technology;

1-2 research reports on deep learning based resource management

architecture design, including candidate schemes of optimal carrier grade

resource management;

2 algorithms analysis reports and verification about carrier grade cloud

resource management based on deep learning, such as failure prediction;










Phase 1 (~2 months): Survey on the carrier grade cloud resource

management based on deep learning technology, including industry and

academia area;

Phase 2 (~7 months): Research on carrier grade cloud resource management

based on deep learning, including architecture design, model selection,


Phase 3 (~3 months): verification of the proposed algorithms.



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HIRPO20160210: Game Theory based Network Slicing

Management



3 Background

NFV change the way how carrier networks are architected by separating

software and hardware and leveraging virtualization technology. It brings great

flexibility, reduces service deployment complexity and speeds up service

deployment. To obtain these benefits, however, network operators have to

spend huge amount of money on carrier grade servers and operation &

maintenance. To further reduce expenses, their network could be constructed

on the public cloud. Therefore, a slice resource management mechanism that

take advantage of the public cloud service should be established to guarantee

a carrier grade service.

4 Scope

1). VM auction and pricing modeling: VM pricing based on demand and

supply in the market, take customized VM and resources in geo-distributed DC

into consideration; game theory based auction mechanism, maximize revenue

of both provider and users, support VM auction on demand, combinatorial

auction of customized VMs and online auction;

2). Deployment algorithm in public cloud: analyze the number of users and

cloud resource consumption model, determine the required VM template and


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combinatorial VM templates for a service based on traffic model; optimal

service deployment cross-DC with low latency;

3). Modeling of elastic scaling scenarios in public cloud: consider VM cost,

auto-scaling overheads, SLA violation penalty, minimize the expenses and

maximize the resource utilization;

4). According to the analysis above, validating the algorithm and solution in

public cloud.


1 survey reports on game theory based network slicing management;

1-2 research reports on network slicing management architecture design,

including candidate schemes of optimal network slicing management;

2 algorithms analysis reports and verification for proposed algorithms;










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Phase 1 (~2 months): Survey on game theory based network slicing

management, including industry and academia area;

Phase 2 (~7 months): Research on game theory based network slicing

management, including architecture design, model selection, algorithm design

and so on;




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HIRPO20160211: Resource Allocation and Mapping for

Network Slices



3 Background

The integration of vertical markets (e.g. smart car, e-health, smart city, Internet

of Things, etc.) and the ability to support real-time critical services (e.g. virtual

reality office, real-time remote computing for mobile terminal, traffic safety and

efficiency etc.) are needed in 5G network era. Network slices as an end-to-end

virtual resource for connection will satisfy these diverse service requirements.

Virtual resources allocation and mapping for carrier grade network calls for

higher reliability. Virtual resource allocation and mapping management for

network slices serve as a key component in 5G network operation. Therefore

designing an optimal virtual resource allocation and mapping management

algorithm for network slices are of vital significance.

4 Scope

A. Survey and analysis of resource allocation and mapping management

algorithm, as well as research of the industry’s trend to implement virtual

resource allocation and mapping management;

B. Detailed definition of the environment and constraints for network slice

resource allocation and mapping, plus analysis of resource modeling;

C. Design the resource allocation and mapping management algorithms for

network slices, which meet the following requirements: fulfills


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telecommunications reliability requirement, effectively utilizes existing physical

resources and reduces resource fragments. The objectives considered include

resource utilization, load balance, reliability and energy efficiency, etc.


1 survey and report on resource allocation and mapping for network

slices;1-2 research reports on resource allocation and mapping for

network slices, including different algorithms for optimal resource

allocation and mapping. In addition, explain how the proposed algorithm

can be adapted and verified on the dynamic industry with flexibility;










Phase 1 (~2 months): Survey on resource allocation and mapping for network

slices, including industry and academia area;

Phase 2 (~7 months): Research on resource allocation and mapping for

network slices, including architecture design, model selection, algorithm

design and so on;


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HIRPO20160212: Trajectory Modeling and Generation

for Mobile Users


2 Subject: others

3 Background

Location is a unique asset for mobile operators, with wide coverage,

all-weather, high frequency, etc. Open location ability through open API can

create new revenue model. However the accuracy of user position acquired

from network is not high, that is why we need the trajectory recovery algorithm.

It requires a lot of location data of multi users to refine relative algorithm, but it

is hard to get those data. Obtain large quantities of user location data through

simulation has a positive impact on the trajectory recovery algorithm.

4 Scope

Investigation and analysis of the current mobile user tracking information,

include accuracy, sampling rate, magnitude, cost, as well as the content

and sampling rate of MR (measurement report);

Analysis of Integrated map data in simulation platform, and generate user

trajectory automatically by map data;

Modeling trajectory in simulation platform, and build large-scale scenarios,

output a large number of user trajectory, align with trajectory recovery

algorithm.


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1 survey reports on trajectory modeling and generation for mobile users;

1-2 research reports on architecture design of trajectory modeling and

generation for mobile users, including candidate schemes of optimal

trajectory generation;

2 algorithms analysis reports and verification about proposed algorithms;










Phase 1 (~2 months): Survey on trajectory modeling and generation for mobile

users, including industry and academia area;

Phase 2 (~7 months): Research on trajectory modeling and generation for

mobile users, including architecture design, model selection, and algorithm

design and so on;

Phase 3 (~3 months): Verification of the proposed algorithms.


call for proposals - /media/corporate/pdf/hirp/2016--future... · unless otherwise agreed by huawei...

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